3 Executive Summary It is routinely asserted in courts, journals and the media that it makes no difference whether a child has a mother and a father, two fathers, or two mothers. Reference is often made to social-scientific studies that are claimed to have demonstrated this. An objective analysis, however, demonstrates that there is no basis for this assertion. The studies on which such claims are based are all gravely deficient. Robert Lerner, Ph.D., and Althea Nagai, Ph.D., professionals in the field of quantitative analysis, evaluated 49 empirical studies on same-sex (or homosexual) parenting. The evaluation looks at how each study carries out six key research tasks: (1) formulating a hypothesis and research design; (2) controlling for unrelated effects; (3) measuring concepts (bias, reliability and validity); (4) sampling; (5) statistical testing; and (6) addressing the problem of false negatives (statistical power). Each chapter of the evaluation describes and evaluates how the studies utilized one of these research steps. Along the way, Lerner and Nagai offer pointers for how future studies can be more competently done. Some major problems uncovered in the studies include the following: Unclear hypotheses and research designs Missing or inadequate comparison groups Self-constructed, unreliable and invalid measurements n-random samples, including participants who recruit other participants Samples too small to yield meaningful results Missing or inadequate statistical analysis Lerner and Nagai found at least one fatal research flaw in all fortynine studies. As a result, they conclude that no generalizations can reliably be made based on any of these studies. For these reasons the studies are no basis for good science or good public policy. Four Appendices follow. Appendix 1 is a bibliography of the studies and related publications. Appendix 2 is a table that summarizes the evaluation of each of the studies with regard to each research step. Appendix 3 (by William C. Duncan) is an overview of how these studies have been used in the law. Appendix 4 (by Kristina Mirus) describes how the media has covered these studies. 3

4 Foreword By David Orgon Coolidge Director, Marriage Law Project What do existing studies tell us about the impact of same-sex parenting on children? thing. That s right, nothing. You would never know that, however, if you were to read most court decisions, law review articles, commission reports or newspaper articles. You would hear the opposite. The point of the study which follows is not to try to answer the question, Why is this? Instead, Robert Lerner and Althea Nagai have simply evaluated the studies themselves. They have asked: What are their hypotheses? How do they set about to prove them? What do they conclude? In formulating, executing and analyzing their research, do these studies get it right? The results are not pretty. Lerner and Nagai identified 49 empirical studies on the subject of same-sex parenting.* After going through them all, inch-by-inch, they found nothing. I first saw the need for such an evaluation back in 1996, in Honolulu, Hawaii. I sat through two weeks of testimony in the samesex marriage case, Baehr v. Miike. Almost all of the testimony was * The terms homosexual (on the one hand) and gay and lesbian (on the other) are both loaded. The studies evaluated here examine parenting by same-sex couples in sexual relationships. To avoid distraction I have used the term same-sex. 4

5 by social scientists. It raised questions I could not shake. Many of those questions are larger ones, such as how science and morality relate. But other questions were more straightforward: Are these studies well-done by normal standards? Should journals publish them? Should policymakers rely on them? The fact of the matter is that many people, including policymakers, are relying upon these studies in litigation, legislation, scholarly writing, and in the larger public debate. (To confirm this, see Appendices Three and Four by Bill Duncan and Kristina Mirus.) The least that should be done is to take a serious look at the methodology of the studies. That is what Robert Lerner and Althea Nagai have done. At the risk of damaging their professional and academic reputations, they have done this full-scale evaluation. Here you have the results. You will learn more than you ever wanted to know about how studies should be designed, implemented and evaluated. And you will learn how even the best studies of same-sex parenting fall far short of these standards. Lerner and Nagai have not only taken apart existing studies, however. By setting their evaluation in the context of a broader discussion of social-scientific research, they have pointed the way toward better studies. They are clearing ground so others can go forward. In the meantime, the rest of us have decisions to make. How shall we proceed? Lerner and Nagai make no attempt to answer this question. They have only one point to make: Whatever you do, don t do it based on these studies. Take the time to see what Lerner and Nagai discovered about the same-sex parenting studies. These authors know a better or worse study when they see it, and they tell it like it is. Whether we like it or not, we are all in their debt. 5

6 Basis: What the Studies Don t Tell Us About Same-Sex Parenting By Robert Lerner, Ph.D., and Althea K. Nagai, Ph.D. Introduction [C]hildren with two parents of the same gender are as well adjusted as children with one of each kind. 1 This view, revolutionary in its implications, and unheard of five years ago, is now commonly asserted by social scientists, lawyers, policymakers and the media. Numerous studies are routinely offered to show that the sexual orientation of a couple makes no difference to the well-being of children. The obvious implication of this view is that two gay dads or two lesbian moms can raise a child as well as can two married biological parents. Simply being surrounded by two caring adults is thought to be enough to raise most children to be healthy, well-adjusted adults. 2 Is this claim true? Does the research supporting it stand up to scientific scrutiny? These are the questions discussed in this study. Our approach to this question concentrates on an analysis of the methodologies used to carry out existing same-sex parenting studies. We conclude that the methods used in these studies are so flawed that these studies prove nothing. Therefore, they should not be used in legal cases to make any argument about homosexual vs. heterosexual parenting. 3 Their claims have no basis. 4 What Social Science Requires Social science research is a complex process, but it follows a series of well-defined steps. Each of these steps must be carried out properly to obtain valid conclusions. Like a chain is only as strong as its tes for this section begin on Page 9 6

7 weakest link, the conclusions derived from any research study are only as reliable as its weakest part. 5 The typical sequence of social-scientific research involves: Formulating concepts and research hypotheses Creating the research design Establishing measurements for important concepts Defining the sample and its selection procedures Collecting the data Performing statistical tests on the data analysis, and Based on the above, hopefully reaching valid conclusions. The studies discussed here will be analyzed by following the typical sequence of social science research methods textbooks. Under each heading we will analyze all the studies to see how well they meet accepted social science standards. Any failures in the processfailure to properly design the study, failure to properly measure the relevant variables, failure to properly control for extraneous variables, and failure to use the proper statistical tests-make a study scientifically invalid. Most importantly of all, if a study claims to find no difference i.e. non-significant results, and that study failed to carry out one or more of these research links in the proper manner, its conclusions are purely and simply invalid. Why? Because failing to carry out correctly one or more of these essential elements, in and of itself increases the chances of finding non-significant results. In other words, if you look for wrong findings using wrong methods, it is even more likely you ll get wrong results. Social Science and Public Policy With one exception, the authors of these studies wish to influence public policy to support same-sex marriage and the adoption of children by homosexual couples. While the authors of these studies have every right to advocate this point of view, as do those who disagree with them, their wish means that the stakes in obtaining valid answers to these research questions are very high. It is not enough for a study to be interesting, or raise important questions about a subject, or to be provocative. While these criteria may be enough to 7

8 get a study published, they are not strong enough to justify dramatic alterations in long-established public policies. To justify changes in public policy, studies should be strong enough that policy makers have faith in the study s reliability, and confidence that more research is unlikely to overturn its findings. This is not an unreasonable requirement. The public policy consequences of relying on inadequate or insufficient studies can be devastating. In 1973, a literature review undertaken by social scientists Elizabeth Hertzog and Cecelia Sudia purported to find that the effects of growing up in fatherless homes are at most minimal and likely to be due to other factors. The authors did not stop here. They stated it might be a good idea to increase community support for single parents, 6 rather than developing policies that forestall the absence of fathers, or that oppose easy divorce. This study was part of a larger current of expert opinion proclaiming that growing up in a oneparent family had no negative consequences for the children living in these arrangements. With more rigorous research, these interpretations were challenged and eventually overthrown. Research has demonstrated that divorce is not the costless exercise for children that many had proclaimed it to be. The newer research demonstrated that children growing up in fatherless families do not do as well financially, in school, and emotionally both as children and as adults, as those in families with their married biological parents. 7 Therefore, the standards used here, to investigate studies on the impact of same-sex parenting on children, are necessarily demanding. We owe ourselves nothing less. How These Studies Were Selected All of the articles used in this review deal with same-sex couples and/or their children. We excluded dissertations, review articles, and articles in the nonscientific press. 8 We have only analyzed reports of original research studies (i.e., real social science). We have tried to be as exhaustive as possible, although research is exploding in this field. 8

9 Working from a variety of angles, 9 we arrived at a final list of 49 studies for analysis that have been either published in professional journals or as chapters in a book. 10 All present the results of original research on homosexual parents and/or their children. Do these 49 studies offer conclusive proof that there is no difference between heterosexual and homosexual households? We believe that these studies offer no basis for that conclusion because they are so deeply flawed pieces of research. The reader is invited to make his or her own judgment. tes to Introduction 1. Harris, 1998, p. 51. Harris cites this body of studies in her controversial book on child development. 2. For example, sociologist Judith Stacey, writing in a recent issue of Contemporary Sociology, a book review journal, that focuses on sociology and public policy, writes that thus far the research on the effects of lesbian parenting on child development is remarkably positive and therefore challenging [the status quo]... Stacey, 1999, p This is not the same as concluding that traditional family arrangements are better. It simply states that the evidence presented above does not justify the opposite conclusion. 4. Since vocabulary related to homosexuality is extremely contentious, we should explain our terminology. We have tried to generally use the term samesex, since the terms gay and lesbian (on the one hand) and homosexual and heterosexual (on the other hand) are so ideologically polarized. However, the studies themselves use one set of terms or the other, so the reader should expect a variety of terms. 5. For example, one can have a perfectly selected sample, but concepts that are so badly defined and poorly measured that one is unable to conclude anything from the results of the study. 6. Cited and discussed in Popenoe, 1998, pp ; McLanahan and Sandefur, 1994, pp A well-known study in the same vein was sociologist Jessie Bernard s The Future of Marriage (1972), which became famous or infamous for its comment that to be happy in a traditional marriage a woman must be mentally ill (quoted in Whitehead, 1998, p. 51). 7. For detailed discussion of the extensive research literature see the following 9

10 works: Waite, 1995; McLanahan and Sandefur, 1994; Popenoe, 1998; Amato and Booth, 1997; and Whitehead, Dissertations are original studies, not review articles; but if they go unpublished, the most one can say is that they met the minimum standards for receiving a degree from the university that granted them, and nothing more. Review articles were excluded because they present no original data for assessment. Articles found in the nonscientific press were excluded because their criteria for publication (e.g., popular interest, immediate policy relevance) are not the same as those for assessing the scientific credibility of a study. 9. Graciela Ortiz, M.S.W., conducted initial bibliographic research in the summer of Additional studies were identified by examining law review articles published by Wardle (1997) and Ball and Pea (1998), briefs filed in Baker v. State, the Vermont same-sex marriage lawsuit, and Lesbian and Gay Parenting: A Resource for Psychologists, Washington, D.C.: American Psychological Association, There is also one book, Tasker and Golombok (1997), which is part of the study. 10

11 Chapter 1 The Good, the Bad, or the Ugly: Formulating the Hypothesis We ve all heard the slogans: If you don t know where you re going, you can count on getting there, or If you aim for nothing, you re sure to hit it. The same is true for formulating the hypothesis of a research study: If your goal is to prove no differences, you re bound to reach it. But you won t have proved no difference, only no basis. All good studies begin with careful definitions of key concepts and careful delineation of the relationship between these concepts. Formulating the hypothesis is the crux of any scientific design, and its development requires special care. The hypothesis determines the main focus of the study, and frames all subsequent research endeavors. 1 Hypotheses can be Good (affirmative), Bad (fuzzy), or Ugly (null). Of the 49 studies, two are Good, 29 are Bad, and 18 are Ugly. Understanding why requires Social Science Research Methods 101, which we will sprinkle throughout this and other chapters. What is a Good Hypothesis? All good social science studies have at their core a positive hypothesis statement. This takes the form of an explicit conceptual relationship between two variables whereby something (an independent variable) causes something else (a dependent variable). 2 The researcher posits a direct relationship between the independent and the dependent variables. 3 The hypothesis can and tes for this section begin on Page 22 11

12 should be stated as a proposition that takes the following form: the greater the a, the greater the b, where a is the independent variable and b is the dependent variable. 4 Hypothesis statements may be either quantitative or qualitative. Consider the following example. A study group of children is enrolled in a social program such as Head Start, while a control group of children is not enrolled there. The independent variable here therefore, is enrolled versus not enrolled. The dependent variable might be something like readiness for school. Assuming that readiness for school is a quantitative variable (i.e., it can be scored on a three or more point scale), then the research hypothesis would compare mean levels of school readiness of those in Head Start with those who are not. Assuming readiness for school is a qualitative (i.e., yes or no ) variable, the research hypothesis would compare the proportion of Head Start children who are ready for school with the proportion who are not ready. 5 There are many different possible hypotheses a reseacher might have, depending upon the nature of the problem studied and the level of measurement assumed in the independent and dependent variables. 6 Applying this view to studying a parent s sexual identity and its possible relationship to child outcomes, the investigator should define conceptually the independent variable ( homosexual versus heterosexual identity), 7 the dependent variable (such as a child s sexual identity, child s psychological adjustment, or the child s sexual behavior), and the posited relationship between the independent variable and the dependent variable(s). An example of such a properly stated research hypothesis is: the children of homosexual parents are more likely to grow up to be homosexual than are the children of heterosexual parents. Good: The Affirmative Research Hypothesis Only two studies among the 49 studies we examined actually contain an explicit positive hypothesis statement of this sort (Pagelow, 1980 and Miller, 1979). 8 Pagelow (1980) hypothesizes that lesbian mothers are more oppressed than heterosexual mothers. The researcher then seeks to measure this by the concept of perceived oppression in the areas of freedom of association, employment, housing, and child custody. 9 12

13 Miller (1979) comes closest to presenting a hypothesis in the proper format: Miller asks, A.) Do gay fathers have children to cover their homosexuality? B.) Do they molest their children? C.) Do their children turn out to be disproportionately homosexual? D.) Do they expose their children to homophobic harassment? 10 While Miller does not put his hypotheses in precisely the y=(f)x format, the hypothesis statements are both specific and decisional (i.e., they can be answered as either yes or no regarding the homosexuality of the father). Thus Miller s statements can be easily rephrased into the following testable hypotheses: A.) The reason for gay men having children is to cover their homosexuality (as opposed to other choices provided by the investigator, such as he loved the woman, he was confused, he just wanted children, don t know); B.) Gay fathers are more likely to molest children than are straight fathers; C.) Children of gay fathers are more likely to be homosexual than are children of straight fathers; and D.) Children of gay fathers are more likely to be exposed to homophobic harassment than are children of straight fathers. 11 Stated in this form, the hypotheses can then be verified or refuted by empirical research. Bad: The Fuzzy Hypothesis A majority of the studies we examined (29 of them or 59 percent) failed to produce a testable hypothesis. Of these, 12 studies rendered their statement of the research problem in the form of Are there differences between homosexual and heterosexual parents? 12 For example, Bigner and Jacobsen, 1989a state their research problem as, an examination of factors that may motivate gay men to become parents, and to explore whether gay fathers may differ from heterosexual fathers regarding the value of children in their life as an adult. 13 Brewaeys et al. (1997) poses the problem as an examination of family relationships and emotional/behavioral and gender role development of 4-8 year old children 14 in lesbian donor-inseminated families, compared to heterosexual families who conceived their child also by donor insemination and heterosexual families who conceived their child naturally. Hypotheses that are stated in the form of looking for possible differences do not suffice as statements of research hypotheses. Formulation of a hypothesis in terms of pos- 13

14 sible differences fails to address any of the causal questions that guide hypothesis formation. Such a formulation is purely descriptive in nature and is not an explicit conceptual relationship between two variables whereby something (an independent variable) causes something else (a dependent variable). 15 This kind of formulation, which may seem commendable in its caution, fails the so what? test. A proper research hypothesis requires the hypothesizing of some kind of causal mechanism operating in the real world so that some kind of tentative causal conclusion can be drawn from the research results if they are valid and the hypothesis test is successful. 16 Seventeen studies present the research problem in the form of, what are the characteristics? 17 For example, Gartrell states, The aim [of this study] was to learn about the homes, families, and communities into which the children were to be born. 18 McCandish writes, The family dynamics and developmental changes within these families and the implications for the psychotherapeutic treatment of lesbian mother families are the subject of this chapter. 19 Pennington declares, The purpose of this chapter is to discuss the major issues confronted by children living in lesbian mother households. 20 Hypotheses that take the form of descriptions of characteristics face a different problem from that faced by statements of possible differences. A focus on what is characteristic of a population (e.g., the mean, median, or mode) can obscure causal relations that are not characteristic of the populations studied, but are nonetheless causal in nature. 21 For example, sociologists Sara McLanahan and Gary Sandefur report that 29 percent of young adults from one-parent families dropped out of high school while only 13 percent of those from two-parent families dropped out. Dropping out is not characteristic of children from either type of family structure, yet there is little doubt that a causal relationship between the variables of type of family structure and the propensity to drop out of school exists (1994, Figure 1, p. 41). This problem can be put in more general terms. Focusing on characteristics of populations obscures the necessity for a proper research hypothesis to focus on the relationship between two variables and not the properties of each of 14

15 them considered separately. In this respect, focusing on the characteristics of an attribute is misleading and hinders the scientific research enterprise. All of these studies are not a priori invalid as instances of exploratory research. Compared to studies that state and test the research hypothesis properly, however, they are much inferior in their level of research sophistication and precision. It tells us to look for and expect other problems in later research steps. Authors with such weaknesses in their formulation of hypotheses are unlikely to produce any conclusions sufficiently robust so to inform public policy debates with any degree of dependability. The Ugly: Affirming The Null Hypothesis The remaining 18 studies explicitly seek to find no differences between heterosexual and homosexual parents in child outcomes and to make this formulation a kind of hypothesis statement. While this procedure is superior in some way to those used in the other studies, it is also highly problematic because of the difficulties associated with testing hypotheses purporting to affirm the null hypothesis. The authors of the null hypothesis-affirming studies seek to show either that children raised by homosexual couples are not more likely to grow up to be homosexual themselves than are those raised by heterosexual couples, and/or that they are not more likely to grow up with psychological problems than are children raised by heterosexual couples, or both. Eighteen studies explicitly seek to find no differences between heterosexuals and homosexuals. 22 For example, Flaks et al, in their study of 15 lesbian couples, 15 heterosexual couples and their children, state, On the basis of prior research, we expected [to find] no differences between the children of lesbian and heterosexual parents in any of the areas evaluated. 23 In another case, Huggins studied adolescent children of lesbians, expecting that a parent s homosexuality would not result in confusion of the child s sexual identity, inappropriate gender role behavior, sexual orientation, and overall psychopathology. 24 Likewise, Patterson s studies of donor-inseminated lesbian families all start with the expectation of finding no differences between 15

16 the children of lesbian and heterosexual parents. 25 The same is also the case with Tasker and Golombok (1995, 1997). The no difference hypothesis used in the 18 studies discussed above inverts the usual social science quantitative research procedure, which would use a positive research hypothesis in the form described above. This creates two major methodological problems that are unrecognized by the all the authors of these studies save one (Chan et al, 1998). 1) Failing to reject the null hypothesis necessarily leads to an indeterminate result because one cannot validly confirm the null hypothesis, and 2) Inverting the normal hypothesis testing situation makes it too easy to fail to reject the null hypothesis, which is the outcome favored by these researchers. This results in an undue partiality in interpreting their research findings and in carrying out the research itself. To see all of this clearly, it is necessary to review the usual statistical testing procedure in quantitative social science. This procedure requires statistical testing of a positive research hypothesis. A simplified example may help to visualize what is involved. For example, suppose a researcher hypothesizes that political liberalism leads to greater support for abortion rights than does political conservatism. One way to test this research hypothesis might be to use a national sample survey of the American public (e.g., data from the General Social Survey produced by the National Opinion Research Center). With this body of data, which consists of individual responses to questions on a questionnaire, one computes the mean support for abortion score of liberals and the mean support for abortion score for conservatives. 26 One can assume that if this procedure is carried out, liberals will have a higher average score than do conservatives (and in fact, they do). Since it is extremely unlikely that such a comparison will yield exactly the same average score for both liberals and conservatives, one must question whether this finding is a real difference or whether it could be due to chance factors. The difference in averages alone does not provide sufficient information to determining the likelihood. To answer the question, statisticians 16

17 have developed ways of distinguishing between statistically significant and statistically insignificant differences. Insignificant differences might be due to sampling error, measurement error, or just random fluctuations. In fact, these are competing claims that ought to be considered in as possible explanations for any research finding. Another way to state the null claim is, that if it is true, any difference found in the data is due solely to random variation. Chance occurrences of this kind do happen; individuals do win the lottery and draw royal flushes in honest poker games. To ascertain whether in a given instance random variation explains the findings in the data, the researcher carries out a statistical hypothesis test. This requires a research hypothesis 27 and a null hypothesis. The research hypothesis is of the kind already discussed. The null hypothesis is a hypothesis of no difference or no effect. In the abortion example, the research hypothesis is that liberals are more proabortion than are conservatives. 28 The null hypothesis is that there is no difference between liberals and conservatives in their support for abortion rights. Both of these cannot be true. In carrying out statistical hypothesis testing, the null hypothesis is a statistical device that allows for calculation of the value of a test statistic. The test statistic is calculated to determine the probability that the null hypothesis is true given the data at hand. After the calculations are carried out, the test statistic yields some number. If the number calculated from the test statistic is greater than a certain preset value, which is called the critical value (e.g., t>=2.00), the null hypothesis is rejected at the associated level of statistical significance (e.g., p<.05) 29 and the research hypothesis is accepted. For example, suppose we find that the mean abortion rights score for liberals is greater than it is for conservatives. In our example, the relevant test statistic is calculated and the results are checked to see whether they are statistically significant or not at the preset level of statistical significance (in fact, there is a substantial statistically significant difference between liberals and conservatives when this test is carried out). Then we reject the null hypothesis and accept the alternative hypothesis that liberalism is correlated with support for abortion rights. Of course, carrying out this one hypothesis test does not end the researcher s task. In fact, the formal hypothesis test 17

18 is just the initial step in analyzing the data. The social scientist then has to show that this difference is not due to other factors (for example, due to differences in education among sample members or to selection biases in the sample). However, at least he has a statistical relationship to work with, to try to either explain or explain away in terms of broader substantive considerations. There are two subsidiary points that need to be made here. First, the reason the statistical test situation is conceived in the above manner is to yield a determinate outcome. If the null hypothesis is rejected, then the alternative hypothesis is accepted. In our example above, we reject the null hypothesis of no difference between liberals and conservative on their support for abortion rights and accept the alternative hypothesis that liberals have a higher average support for abortion rights scores than do conservatives which is statistically significant (and they really do). Also, statistical tests of this kind place the burden of proof on the investigator to show support for his research hypothesis. That is why the criterion for rejecting the null hypothesis is difficult. Thus, social science researchers conventionally use the p<.05 level of statistical significance. It does not have to be this stringent (1 out of 20), but the practice has evolved so that it has become the standard social science research convention in the standard positive hypothesis social science research situation. 30 What happens if the null hypothesis cannot be rejected? In this situation there are always two competing explanations for this result. The first possible explanation for the failure to reject the null hypothesis is that whatever differences are found really are due to chance factors, so that no statistical, let alone causal, relationship between the two variables really exists. A statistician might say that if the test were repeated an infinite number of times, a zero correlation or a zero difference between the two groups studied would result. The examples of the honest poker game and the honest lottery are relevant here. The second possibility is that the research hypothesis is true but its truth cannot be ascertained by the research results because there is some flaw in the study design or in the statistical test itself, which causes the test statistic to yield a statistically insignificant result or p value. 31 Therefore, failing to reject the null hypothesis by itself does not lead to a determinate result. Since every failure to reject the null hypothesis has two possible explanations, one can- 18

19 not simply accept the null hypothesis in the same way that one can reject the null hypothesis and accept the alternative hypothesis. Further investigation or conducting a new study is always in order. This is the problem with the 18 studies that explicitly sought to confirm the null hypothesis as their research hypothesis. These studies sought to prove the null hypothesis, which, as we have shown, is not the same thing as failing to reject the null hypothesis. In substantive terms, their authors seek to show that homosexual parents produce the same child outcomes as do heterosexual parents. This means that they desire to be able to accept the null hypothesis as showing that homosexual parenting has no effect on child outcomes simply on the basis of failing to reject the null hypothesis. This violates the standard statistical hypothesis testing procedure. It is wrong because, as we show above, failing to reject the null hypothesis does not necessarily mean that the null is true. 32 This is not merely a technical flaw in these studies. These investigators report their failure to reject the null hypothesis and falsely conclude that there is no difference between homosexual and heterosexual parents in child outcomes. 33 This false conclusion invalidates the findings of no difference between heterosexual and homosexual parents as reported in the research literature that we have surveyed. Only the authors of one study (Chan et. al, 1998) showed any awareness of the problem, but they did nothing to correct for it or to alter their interpretations of their results because of it. If the null hypothesis itself becomes the research hypothesis, and some kind of research hypothesis is to become the new null hypothesis, then the standard testing situation must be radically altered to accommodate this situation and non-standard statistical tools are needed in order to reach defensible results. 34 The studies we surveyed all failed to do this or even to indicate that they saw the need for doing it. This indicates that their authors understanding of the logic of quantitative social scientific research is suspect. When the hypothesis statement is properly conceptualized, the null hypothesis is used in conducting statistical tests as the comparison hypothesis to the one under investigation. It is no substitute for a properly formulated affirmative hypothesis. It is the objective of properly stated hypotheses, proper design, and proper execution of an empirical research 19

20 study to decrease the probability that the relationship uncovered by the investigator is due to chance. 35 The goal of genuine social-scientific research, in short, is to make the null hypothesis less, not more, likely. 36 Properly speaking, then, one can never prove the validity of the null hypothesis. When you hear the statement that a study found no significant difference, what this actually means is that, having done some tests, the investigator can only say, I looked for differences, and haven t found anything significant yet. But who knows? In social-scientific terms, the study failed to reject the null hypothesis. It proved nothing. 37 In summary, in conducting a statistical test of a hypothesis there are two possible outcomes. The first is to be able to reject the null hypothesis and accept the research hypothesis that a difference between the groups does exist that is not likely to be due to chance factors. The researcher then proceeds to see if his or her hypothesis can stand up to other tests of its validity, by introducing controls for extraneous and confounding factors and the like. These are all the subsequent research steps we will be discussing below. The second possible outcome is to fail to be able to reject the null hypothesis. This is NOT the same as showing that no effect exists. There are many possible reasons why one may fail to reject the null hypothesis yet be in error in doing so. For example, the sample used in the study may be too small to reach the appropriate level of statistical significance for a given effect, the significance level used in the significance test itself may be set too high, or the research instruments used to measure the independent and dependent variables may so highly unreliable that no stable results are possible. Even if none of these factors can explain the absence of positive results, this still does not show that no effect exists. The researcher then proceeds to see if his or her non-finding can stand up to additional tests of its validity, by introducing controls for extraneous and confounding factors that might cause a spurious non-correlation (see more below). Precisely because the usual and correct research procedure is to try to reject the null hypothesis, projects that aim to demonstrate no significant differences between homosexual and heterosexual parents and/or their children face serious problems. If the investigator starts 20

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